Unmanned Aerial Vehicles (UAVs) are employed in a wide range of applications, both civilian and military, including inspection, intelligence, reconnaissance and rescue missions. UAV designs ranges from large fixed wing jets to smaller rotary wing aircraft with one or more rotors. Progress in the electronics industry over the past decades has made it possible to shrink the components necessary for a UAV system to become palm sized, Micro Air Vehicle (MAV). These vehicles can, for example, lift a camera and transmit images, while still being highly maneuverable. The size and maneuverability make MAVs an ideal tool for close range applications, e.g. reconnaissance, inspection and surveillance.
Modern UAVs operate over longer distances and in all environments, rural or urban. Control of a UAV rely on transmission from onboard sensors to a remote unit, containing a display unit, which is operated by the pilot. The pilot is able to steer the aircraft throughout the entire mission solely relying on the received images.
Sophisticated autopilot systems have also been implemented to assist in keeping the aircraft stable, thus providing easier operational control for the pilot. To function optimally it is necessary for these autopilot systems to know the position of the aircraft, i.e. where it is and has been. This to keep the aircraft stable in relation to a position, but also to counter, for example, a wind blow.
During outdoor operations, one may rely on GPS systems to provide the autopilot with the necessary position, which it then uses for further calculations. However, recent advances in UAV design and new application areas also makes it desirable to operate in areas with low or no GPS coverage, or to increase the accuracy of the autopilot system. For example, GPS signal reception indoors is poor or non-existing; requiring other means of determining the necessary information.
To increase the accuracy or operate in areas with low or no GPS, the autopilot system needs to obtain a position by some other means. Images captured at different times can be used to determine the 3D translation and rotation of the camera from one image to another. However, images have an inherent scale ambiguity, so in order for the autopilot system to utilize the translation thus obtained it must first be converted into the correct scale. This can be done with a scale factor, which when multiplied with the translation, converts the translation into the desired unit. For practical purposes, measurements will in this document be referred in the metric system. Such a scale factor can be calculated if one knows the depth, i.e. distance, from the camera to one or several features in the images. A feature can be any detectable object or structure that is possible to identify in the images.
Several solutions offer to produce the depth measurements required to calculate the scale factor by utilizing the onboard camera capabilities. An example of one of these solutions, utilize a stereo vision system with two cameras in each relevant orientation. By comparing two or more pictures captured by camera A and B at the same time with known translation from camera A to camera B, a depth to each feature is determined. This solution does however require two separate cameras in each of the directions were one would want to measure the depth, complicating the UAV system unnecessarily. With the strict weight and size requirements of MAVs, it is often not possible to include this system in the design.
Another example of a system that addresses this issue is monocular vision depth estimation. This solution utilize a so-called Kalman filter per feature calculation, providing the depth through a series of measurements over time. This, however, implies that the system will have to observe each feature for a certain number of time steps to be able to calculate the actual depth. Thus, the solution is computationally expensive.
Therefore, there is a need for a system that can calculate the depth for use in a UAV system that is computationally less expensive and requires few additional means, but still meets the requirements of modern day UAVs.